
import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


lowercase    :   Any						=			trt.Logger(trt.Logger.WARNING)
lowercase    :   str						=			absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowercase    :   Optional[int]						=			logging.getLogger(__name__)

lowercase    :   Any						=			argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

lowercase    :   Union[str, Any]						=			parser.parse_args()

if args.tokenizer_name:
		lowercase    :   Optional[Any]						=			AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
		raise ValueError(
		    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
		    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
		)

logger.info('Training/evaluation parameters %s', args)

lowercase    :   List[str]						=			args.per_device_eval_batch_size

lowercase    :   Optional[Any]						=			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowercase    :   Union[str, Any]						=			True

lowercase    :   int						=			'temp_engine/bert-fp32.engine'
if args.fpaa:
		lowercase    :   Optional[Any]						=			'temp_engine/bert-fp16.engine'
if args.inta:
		lowercase    :   int						=			'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
		os.makedirs('temp_engine')

lowercase    :   Any						=			1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
		with open(args.onnx_model_path, 'rb') as model:
				if not parser.parse(model.read()):
						for error in range(parser.num_errors):
								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
		lowercase    :   str						=			[network.get_input(i) for i in range(network.num_inputs)]
		lowercase    :   Dict						=			[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				lowercase    :   str						=			1 << 50
				if STRICT_TYPES:
						config.set_flag(trt.BuilderFlag.STRICT_TYPES)
				if args.fpaa:
						config.set_flag(trt.BuilderFlag.FPaa)
				if args.inta:
						config.set_flag(trt.BuilderFlag.INTa)
				lowercase    :   Dict						=			builder.create_optimization_profile()
				config.add_optimization_profile(profile)
				for i in range(len(input_names)):
						profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
				lowercase    :   str						=			builder.build_engine(network, config)

				# serialize_engine and store in file (can be directly loaded and deserialized):
				with open(engine_name, 'wb') as f:
						f.write(engine.serialize())




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     int		,    _lowerCamelCase					:     str)   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  Tuple       =   np.asarray(inputs["input_ids"]		,    dtype=np.intaa)
							__UpperCamelCase      :  Union[str, Any]       =   np.asarray(inputs["attention_mask"]		,    dtype=np.intaa)
							__UpperCamelCase      :  Optional[Any]       =   np.asarray(inputs["token_type_ids"]		,    dtype=np.intaa)

							# Copy inputs
							cuda.memcpy_htod_async(d_inputs[0]		,    input_ids.ravel()		,    _lowerCamelCase)
							cuda.memcpy_htod_async(d_inputs[1]		,    attention_mask.ravel()		,    _lowerCamelCase)
							cuda.memcpy_htod_async(d_inputs[2]		,    token_type_ids.ravel()		,    _lowerCamelCase)
							# start time
							__UpperCamelCase      :  int       =   time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(_lowerCamelCase) for d_inp in d_inputs] + [int(_lowerCamelCase), int(_lowerCamelCase)]		,    stream_handle=stream.handle)
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
							cuda.memcpy_dtoh_async(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
							# Synchronize the stream and take time
							stream.synchronize()
							# end time
							__UpperCamelCase      :  Optional[Any]       =   time.time()
							__UpperCamelCase      :  Union[str, Any]       =   end_time - start_time
							__UpperCamelCase      :  Tuple       =   (h_outputa, h_outputa)
							# print(outputs)
							return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowercase    :   List[Any]						=			Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
		set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		lowercase    :   str						=			load_dataset(args.dataset_name, args.dataset_config_name)
else:
		raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

lowercase    :   Optional[int]						=			raw_datasets['validation'].column_names

lowercase    :   Optional[int]						=			'question' if 'question' in column_names else column_names[0]
lowercase    :   Dict						=			'context' if 'context' in column_names else column_names[1]
lowercase    :   List[Any]						=			'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowercase    :   List[str]						=			tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
		    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
		)

lowercase    :   Optional[int]						=			min(args.max_seq_length, tokenizer.model_max_length)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any])   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   [q.lstrip() for q in examples[question_column_name]]

							# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
							# in one example possible giving several features when a context is long, each of those features having a
							# context that overlaps a bit the context of the previous feature.
							__UpperCamelCase      :  List[str]       =   tokenizer(
							    examples[question_column_name if pad_on_right else context_column_name]		,    examples[context_column_name if pad_on_right else question_column_name]		,    truncation="only_second" if pad_on_right else "only_first"		,    max_length=_lowerCamelCase		,    stride=args.doc_stride		,    return_overflowing_tokens=_lowerCamelCase		,    return_offsets_mapping=_lowerCamelCase		,    padding="max_length"		,    )

							# Since one example might give us several features if it has a long context, we need a map from a feature to
							# its corresponding example. This key gives us just that.
							__UpperCamelCase      :  Dict       =   tokenized_examples.pop("overflow_to_sample_mapping")

							# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
							# corresponding example_id and we will store the offset mappings.
							__UpperCamelCase      :  str       =   []

							for i in range(len(tokenized_examples["input_ids"])):
														# Grab the sequence corresponding to that example (to know what is the context and what is the question).
														__UpperCamelCase      :  Tuple       =   tokenized_examples.sequence_ids(_lowerCamelCase)
														__UpperCamelCase      :  Dict       =   1 if pad_on_right else 0

														# One example can give several spans, this is the index of the example containing this span of text.
														__UpperCamelCase      :  str       =   sample_mapping[i]
														tokenized_examples["example_id"].append(examples["id"][sample_index])

														# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
														# position is part of the context or not.
														__UpperCamelCase      :  Optional[int]       =   [
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples["offset_mapping"][i])
														]

							return tokenized_examples


lowercase    :   Optional[int]						=			raw_datasets['validation']
# Validation Feature Creation
lowercase    :   List[str]						=			eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

lowercase    :   Any						=			default_data_collator

lowercase    :   Tuple						=			eval_dataset.remove_columns(['example_id', 'offset_mapping'])
lowercase    :   int						=			DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     int="eval")   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   postprocess_qa_predictions(
							    examples=_lowerCamelCase		,    features=_lowerCamelCase		,    predictions=_lowerCamelCase		,    version_2_with_negative=args.version_2_with_negative		,    n_best_size=args.n_best_size		,    max_answer_length=args.max_answer_length		,    null_score_diff_threshold=args.null_score_diff_threshold		,    output_dir=args.output_dir		,    prefix=_lowerCamelCase		,    )
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														__UpperCamelCase      :  int       =   [
														    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
														]
							else:
														__UpperCamelCase      :  Dict       =   [{"id": k, "prediction_text": v} for k, v in predictions.items()]

							__UpperCamelCase      :  str       =   [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=_lowerCamelCase		,    label_ids=_lowerCamelCase)


lowercase    :   Dict						=			load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
		# setup for TRT inferrence
		for i in range(len(input_names)):
				context.set_binding_shape(i, INPUT_SHAPE)
		assert context.all_binding_shapes_specified




		def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any])   ->       Dict:






									'''simple docstring'''





									return trt.volume(engine.get_binding_shape(_lowerCamelCase)) * engine.get_binding_dtype(_lowerCamelCase).itemsize

		# Allocate device memory for inputs and outputs.
		lowercase    :   Any						=			[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

		# Allocate output buffer
		lowercase    :   Optional[int]						=			cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
		lowercase    :   Dict						=			cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
		lowercase    :   List[Any]						=			cuda.mem_alloc(h_outputa.nbytes)
		lowercase    :   Optional[Any]						=			cuda.mem_alloc(h_outputa.nbytes)

		# Create a stream in which to copy inputs/outputs and run inference.
		lowercase    :   Optional[int]						=			cuda.Stream()

		# Evaluation
		logger.info('***** Running Evaluation *****')
		logger.info(f"  Num examples = {len(eval_dataset)}")
		logger.info(f"  Batch size = {args.per_device_eval_batch_size}")

		lowercase    :   Optional[int]						=			0.0
		lowercase    :   Tuple						=			0
		lowercase    :   str						=			timeit.default_timer()

		lowercase    :   List[str]						=			None
		for step, batch in enumerate(eval_dataloader):
				lowercase				,     lowercase    :   int						=			model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				lowercase				,     lowercase    :   Union[str, Any]						=			outputs
				lowercase    :   Tuple						=			torch.tensor(start_logits)
				lowercase    :   List[str]						=			torch.tensor(end_logits)

				# necessary to pad predictions and labels for being gathered
				lowercase    :   Any						=			accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
				lowercase    :   Any						=			accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

				lowercase    :   str						=			(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
				lowercase    :   Optional[Any]						=			logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

		if all_preds is not None:
				lowercase    :   Optional[Any]						=			nested_truncate(all_preds, len(eval_dataset))

		lowercase    :   Optional[int]						=			timeit.default_timer() - start_time
		logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
		# Inference time from TRT
		logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1000 / niter))
		logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))




		logger.info('Total Number of Inference =  %d', niter)

lowercase    :   Optional[Any]						=			post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase    :   List[str]						=			metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




logger.info(f"Evaluation metrics: {eval_metric}")

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)